An efficient and cost-effective method for the purification of human basophils

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Increased expressions of CD123, CD63, CD203c, and Fc epsilon receptor I on blood leukocytes of allergic asthma

Background: Altered basophil identification markers have been discovered to associate with allergic asthma (AA) in recent years. However, little is known about the expression of basophil markers in blood granulocytes.

Aim: To parallel test blood basophils in peripheral blood mononuclear cell (PBMC) and granulocyte populations of patients with AA and AA combined with allergic rhinitis (ARA)

Methods: The expressions of surface molecules were determined via flow cytometry. CD123 expressing cells in blood were isolated using a cell sorting technique, and mouse AA models were employed for in vivo study.

Results: The numbers of CD123⁺HLA-DR⁻ cells in the granulocytes of AA and ARA patients markedly increased. However, only 49.7% of CD123⁺HLA-DR⁻ cells in granulocytes and 99.0% of CD123⁺HLA-DR⁻ cells in PBMCs were basophils. Almost all CD123⁺HLA-DR⁻ cells expressed CD63 regardless in granulocytes or PBMC. The numbers of CD63, Fc epsilon receptor I (FcεRI), and CD203c expressing cells markedly enhanced in CD123⁺HLA-DR⁻ granulocytes of AA and ARA patients. Mean fluorescence intensity (MFI) of CD63 and CD203c expressions on CD123⁺HLA-DR⁻ PBMC and granulocytes of AA and ARA patients dramatically elevated. House dust mite extract (HDME) and Artemisia sieversiana wild allergen extract (ASWE) enhanced the numbers of CD63⁺CD123⁺HLA-DR⁻ granulocytes and PBMC and the MFI of CD203c expression on CD123⁺HLA-DR⁻ granulocyte of AA and ARA patients. Histamine, tryptase, and PGD2 enhanced proportions of CD123⁺ KU812 cells. ASWE- and HDME-induced AA mice showed upregulated CD63 expression on basophils. In conclusion, upregulated expressions of CD123, CD203c, CD63, and FcεRIα in PBMC and granulocytes of patients with AA and ARA suggest that CD123⁺HLA-DR⁻ cells may contribute to the development of AA and ARA.